Skip to main content
SpringerLink
Log in

Pilot tones design using particle swarm optimization for OFDM–IDMA system

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Since the channel estimation performance is directly affected by the pilot positions, it has been a major task in multicarrier transmission schemes to adjust the pattern of pilot distribution for the purpose of minimizing the estimation errors. Therefore, in this paper, we utilize particle swarm optimization (PSO) algorithm for pilot design process in orthogonal frequency division multiplexing–interleave division multiple access (OFDM–IDMA) system. The main contributions of the paper are: (1) increasing the performance of least squares (LS) algorithm used for channel estimation in OFDM–IDMA system by optimizing the pilot positions using PSO algorithm, (2) instead of using MSE itself in which the matrix inversion process is required as the fitness function of PSO, using the upper bound of mean square error (MSE) in order to decrease the system complexity, (3) using two types of channel models known as COST 207 Rural Area and COST 207 Typical Urban in the simulations to be able to support the reliability and stability of the proposed method in different conditions. In the simulations, it is observed from the bit error rate (BER) and MSE graphs that optimizing the placement of pilot tones using our proposed method demonstrates a superior performance compared to the other considered pilot placement strategies by providing a significant increase in the performance of LS algorithm.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Tse D, Viswanath P (2005) Fundamentals of wireless communication. Cambridge University Press, Cambridge

    Book  Google Scholar 

  2. Haykin S, Moher M (2005) Modern wireless communications. Prentice-Hall, Upper Saddle River

    Google Scholar 

  3. Cimini LJ (1985) Analysis and simulation of digital mobile channel using orthogonal frequency division multiplexing. IEEE Trans Commun 33:665–675

    Article  Google Scholar 

  4. Parasad R (2004) OFDM for wireless communications systems. Artech House, Norwood

    Google Scholar 

  5. Subotic V, Primak S (2006) BER analysis of equalized OFDM systems in Nagakami, m < 1 fading. Wirel Pers Commun 40:281–290

    Article  Google Scholar 

  6. Muhammad IG, Tepe KE, Raheem EA (2013) QAM equalization and symbol detection in OFDM systems using extreme learning machine. Neural Comput Appl 22:491–500

    Article  Google Scholar 

  7. Mahafeno I, Langlais C, Jego C (2006) OFDM–IDMA versus IDMA with ISI cancellation for quasi-static Rayleigh fading multipath channels. In: 4th international symposium on turbo codes & related topics, Munich, Germany, pp 3–7

  8. Nozaki M, Yoshizava S, Tanimoto H (2014) VLSI design of an interference canceller for QPSK OFDM–IDMA systems. In: 2014 IEEE Asia pacific conference on circuits and systems, Ishigaki, Japan, pp 715–718

  9. Ping L, Liu L (2004) Analysis and design of IDMA systems based on SNR evolution and power allocation. In: Vehicular technology conference (VTC2004-Fall), Los Angeles, CA, USA, pp 1068–1072

  10. Taspinar N, Simsir S (2017) Channel estimation using an adaptive neuro fuzzy inference system in the OFDM–IDMA system. Turk J Electr Eng Comput Sci 25:352–364

    Article  Google Scholar 

  11. Ping L, Liu L, Wu KY, Leung WK (2006) Interleave-division multiple-access. IEEE Trans Wirel Commun 5:938–947

    Article  Google Scholar 

  12. Ping L, Guo Q, Tong J (2007) The OFDM–IDMA approach to wireless communication system. IEEE Wirel Commun 14:18–24

    Article  Google Scholar 

  13. Kol KV, Mishra A (2013) Discrete wavelet transform based OFDM–IDMA system with AWGN channel. In: 2013 students conference on engineering and systems (SCES), Allahabad, India, pp 1–4

  14. Coleri S, Ergen M, Puri A, Bahai A (2002) Channel estimation techniques based on pilot arrangement in OFDM systems. IEEE Trans Broadcast 48:223–229

    Article  Google Scholar 

  15. Zaier A, Bouallegue R (2011) Channel estimation study for block-pilot insertion in OFDM systems under slowly time varying conditions. Int J Comput Netw Commun 3:39–54

    Article  Google Scholar 

  16. Hsieh MH, Wei CH (1998) Channel estimation for OFDM systems based on comb-type pilot arrangement in frequency selective fading channels. IEEE Trans Consum Electron 44:217–225

    Article  Google Scholar 

  17. Pathak S, Sharma H (2013) Channel estimation in OFDM systems. Int J Adv Res Comput Sci Softw Eng 3:312–327

    Google Scholar 

  18. Vidhya K, Shankarkumar KR (2013) Channel estimation and optimization for pilot design in MIMO OFDM systems. Int J Emerg Technol Adv Eng 3:175–180

    Google Scholar 

  19. Seyman MN, Taspinar N (2011) Particle swarm optimization for pilot tones design in MIMO–OFDM systems. EUROSIP J Adv Signal Process 2011:1–11

    Google Scholar 

  20. Laguna-Sanchez GA, Barron-Fernandez R (2009) Blind channel estimation for power-line communications by a PSO-inspired algorithm. In: IEEE Latin-American conference on communications (LATINCOM ‘09), Medellin, Colombia, pp 1–6

  21. D’orazio L, Sacchi C, Doneli M (2010) Adaptive channel estimation for STBC-OFDM systems based on nature-inspired optimization strategies. In: 3rd international workshop of multiple access communication (MACOM 2010), Barcelona, Spain, pp 188–198

  22. Seyman MN, Taspinar N (2012) Optimization of pilot tones using differential evolution algorithm in MIMO–OFDM systems. Turk J Electr Eng Comput Sci 20:15–23

    Google Scholar 

  23. Abdelkader YM, Jamal EA (2012) Pilot design optimization using modified differential evolution algorithm in SISO and MIMO OFDM systems. J Basic Appl Sci Res 2:6260–6267

    Google Scholar 

  24. Seyman MN, Taspinar N (2013) Pilot tones optimization using Artificial Bee Colony algorithm for MIMO–OFDM systems. Wirel Pers Commun 71:151–163

    Article  Google Scholar 

  25. Chen SM, Chung NY (2006) Forecasting enrollments using high-order fuzzy time series and genetic algorithms. Int J Intell Syst 21:485–501

    Article  Google Scholar 

  26. Chen SM, Chang TH (2001) Finding multiple possible critical paths using fuzzy PERT. IEEE Trans Syst Man Cybern Part B Cybern 31:930–937

    Article  Google Scholar 

  27. Chen SM, Chien CY (2011) Parallelized genetic ant colony systems for solving the traveling salesman problem. Expert Syst Appl 38:3873–3883

    Article  Google Scholar 

  28. Tsai PW, Pan JS, Chen SM, Liao BY, Hao SP (2008) Parallel cat swarm optimization. In: 2008 international conference on machine learning and cybernetics, Kunming, China, pp 3328–3333

  29. Tsai PW, Pan JS, Chen SM, Liao BY (2012) Enhanced parallel cat swarm optimization based on the Taguchi method. Expert Syst Appl 39:6309–6319

    Article  Google Scholar 

  30. Simsir S, Taspinar N (2015) Channel estimation using radial basis function neural network in OFDM–IDMA system. Wirel Pers Commun 85:1883–1893

    Article  Google Scholar 

  31. Bahrumi I, Leus G, Moonen M (2003) Optimal training design for MIMO–OFDM systems in mobile wireless channels. IEEE Trans Signal Process 51:1615–1623

    Article  Google Scholar 

  32. Jordehi AR (2014) Particle swarm optimization for dynamic optimization problems: a review. Neural Comput Appl 25:1507–1516

    Article  Google Scholar 

  33. Eberhart RC, Shi Y (2000) Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the 2000 congress on evolutionary computation, La Jolla, CA, USA, pp 84–88

  34. Sahman MA, Altun AA (2013) Cost optimization of mixed feeds with the particle swarm optimization method. Neural Comput Appl 22:383–390

    Article  Google Scholar 

  35. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE international conference on neural networks IV, Perth, WA, Australia, pp 1942–1948

  36. Eberhart RC, Shi Y (2001) Particle swarm optimization: developments, applications and resources. In: Proceedings of the 2001 congress on evolutionary computation, Seoul, South Korea, pp 81–86

  37. Horn RA, Johnson CR (1985) Matrix analysis. Cambridge University Press, Cambridge

    Book  Google Scholar 

  38. Golup GH, Loan CFV (1996) Matrix computations. Hopkins University Press, Baltimore

    Google Scholar 

  39. Peters G, Weber R (2016) DCC: a framework for dynamic granular clustering. Granul Comput 1:1–11

    Article  Google Scholar 

  40. Livi L, Sadeghian A (2016) Granular computing, computational intelligence, and the analysis of non-geometric input spaces. Granul Comput 1:13–20

    Article  Google Scholar 

  41. Skowron A, Jankowski A, Dutta S (2016) Interactive granular computing. Granul Comput 1:95–113

    Article  Google Scholar 

  42. Dubois D, Prade H (2016) Bridging gaps between several forms of granular computing. Granul Comput 1:115–126

    Article  Google Scholar 

  43. Loia V, D’Aniello G, Gaeta A, Orciuoli F (2016) Enforcing situation awareness with granular computing: a systematic overview and new perspectives. Granul Comput 1:127–143

    Article  Google Scholar 

  44. Yao Y (2016) A triarchic theory of granular computing. Granul Comput 1:145–157

    Article  Google Scholar 

  45. Ciucci D (2016) Orthopairs and granular computing. Granul Comput 1:159–170

    Article  Google Scholar 

  46. Wilke G, Portmann E (2016) Granular computing as a basis of human–data interaction: a cognitive cities use case. Granul Comput 1:181–197

    Article  Google Scholar 

  47. Syau YR, Skowron A, Lin EB (2017) Inclusion degree with variable-precision model in analyzing inconsistent decision tables. Granul Comput 2:65–72

    Article  Google Scholar 

  48. Wang G, Li Y, Li X (2017) Approximation performance of the nonlinear hybrid fuzzy system based on variable universe. Granul Comput 2:73–84

    Article  Google Scholar 

  49. Cai M, Li Q, Lang G (2017) Shadowed sets of dynamic fuzzy sets. Granul Comput 2:85–94

    Article  Google Scholar 

  50. Sanchez MA, Castro JR, Castillo O, Mendoza O, Diaz AR, Melin P (2017) Fuzzy higher type information granules from an uncertainty measurement. Granul Comput 2:95–103

    Article  Google Scholar 

  51. Wang G, Yang J, Xu J (2017) Granular computing: from granularity optimization to multi-granularity joint problem solving. Granul Comput 2:105–120

    Article  Google Scholar 

  52. Singh PK, Kumar CA (2017) Concept lattice reduction using different subset of attributes as information granules. Granul Comput 2:159–173

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant 115E653.

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Faculty of Engineering, Erciyes University, 38039, Kayseri, Turkey

    Necmi Taşpınar & Şakir Şimşir

Authors
  1. Necmi Taşpınar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Şakir Şimşir
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Necmi Taşpınar.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Taşpınar, N., Şimşir, Ş. Pilot tones design using particle swarm optimization for OFDM–IDMA system. Neural Comput & Applic 31, 5299–5308 (2019). https://doi.org/10.1007/s00521-018-3366-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-018-3366-8

Keywords

Search

Navigation